{"id":"W317587060","doi":"10.1007/0-306-47015-2_46","title":"High-Performance Computing for Computer-Aided Diagnosis of Breast Cancer","year":2005,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Breast cancer; Contrast (vision); Computer science; Mammography; Artificial intelligence; Identification (biology); Contrast enhancement; Image quality; Pattern recognition (psychology); Image processing; Computer-aided diagnosis; Computer vision; Image (mathematics); Cancer; Medicine; Radiology; Internal medicine; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007813695,0.000816502,0.001091707,0.0006455793,0.0002508039,0.0003518401,0.003149728,0.001351674,0.00008823458],"category_scores_gemma":[0.00001956818,0.0008639398,0.0003682507,0.000123815,0.0003008045,0.001770461,0.0009236784,0.001849438,0.0000179543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008720304,"about_ca_system_score_gemma":0.000562043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167626,"about_ca_topic_score_gemma":0.0000148394,"domain_scores_codex":[0.9953668,0.00004125791,0.001376824,0.001408499,0.0009451466,0.000861524],"domain_scores_gemma":[0.9960632,0.0003966816,0.001504201,0.001128826,0.000634152,0.0002729907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006018992,0.00001846538,0.000464957,0.0004260869,0.0003449615,0.000002933763,0.0004696337,0.002081724,0.00005681592,0.02710262,0.0952078,0.8737638],"study_design_scores_gemma":[0.004787452,0.0007439831,0.003262377,0.005283389,0.0005332673,0.0002359072,0.00001758573,0.1230837,0.005707186,0.01942823,0.8323742,0.004542759],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01114395,0.004200523,0.6173668,0.008997917,0.03131765,0.007916974,0.00118117,0.003597246,0.3142778],"genre_scores_gemma":[0.3425592,0.001461131,0.1822543,0.009542592,0.02815356,0.001906676,0.0001987568,0.001263,0.4326608],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.869221,"threshold_uncertainty_score":0.9999448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02055257330515737,"score_gpt":0.2484312878357734,"score_spread":0.2278787145306161,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}